How is the accuracy of similarity measures improved in Oracle AI Vector Search?

The accuracy of similarity measures in Oracle AI Vector Search is improved by ensuring that data is properly normalized before processing. Normalization refers to the process of adjusting values in a dataset to a common scale without distorting differences in the ranges of values. This step is critical because it helps in mitigating the effects of skewed distributions, outliers, or varying scales among different features.

When data is normalized, the similarity measures, such as cosine similarity or Euclidean distance, can operate more effectively, reflecting the true proximity between data points. If the data is not normalized, the metrics may be unduly influenced by features with larger ranges, leading to inaccurate similarity assessments.

Proper normalization can involve techniques such as min-max scaling, z-score normalization, or decimal scaling, depending on the nature of the data. By applying these methods, you ensure that each feature contributes equally to the similarity calculations, resulting in more reliable and accurate outcomes in vector search applications.